Publication Type
Journal Article
Version
submittedVersion
Publication Date
11-2020
Abstract
This paper provides the first result for the uniform inference based on nonparametric series estimators in a general time-series setting. We develop a strong approximation theory for sample averages of mixingales with dimensions growing with the sample size. We use this result to justify the asymptotic validity of a uniform confidence band for series estimators and show that it can also be used to conduct nonparametric specification test for conditional moment restrictions. New results on the validity of heteroskedasticity and autocorrelation consistent (HAC) estimators with increasing dimension are established for making feasible inference. An empirical application on the unemployment volatility puzzle for the search and matching model is provided as an illustration.
Keywords
Martingale difference, Mixingale Series estimation, Specification test, Strong approximation, Uniform inference
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
29
Issue
1
First Page
38
Last Page
51
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2019.09.011
Publisher
Elsevier
Citation
LI, Jia and LIAO, Zhipeng.
Uniform nonparametric inference for time series. (2020). Journal of Econometrics. 29, (1), 38-51.
Available at: https://ink.library.smu.edu.sg/soe_research/2589
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2019.09.011